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相关概念视频

Predicting Molecular Geometry02:27

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The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
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A Gran plot is used to predict the equivalence volume or endpoint of a potentiometric or acid-base titration without reaching the endpoint. Typically, titration data is collected as a function of the titrant's volume up to a point less than the equivalence volume and then transformed into a linear format. The straight line is extended to the x-axis, indicating the necessary titrant volume to achieve the equivalence point.
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In healthcare diagnostics, laboratory tests play a crucial role in identifying and diagnosing a wide range of medical conditions. However, interpreting test results is not always straightforward. An abnormal test result does not always confirm the presence of a disease, just as a normal result does not guarantee its absence. To assess the reliability of these diagnostic tools, healthcare practitioners rely on two key statistical indicators: sensitivity and specificity.
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Kinetics describes the rate and path by which a reaction occurs. In contrast, thermodynamics deals with state functions and describes the properties, behavior, and components of a system. It is not concerned with the path taken by the process and cannot address the rate at which a reaction occurs. Although it does provide information about what can happen during a reaction process, it does not describe the detailed steps of what appears on an atomic or a molecular level. On the other hand,...
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Proteins are one of the most abundant organic molecules in living systems and have the most diverse range of functions of all macromolecules. Proteins may be structural, regulatory, contractile, or protective. They may serve in transport, storage, or membranes; or they may be toxins or enzymes. Their structures, like their functions, vary greatly. They are all, however, amino acid polymers arranged in a linear sequence.
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  1. 首页
  3. 利用不同的距离函数来预测esmfold预测的三级结构的几何深度学习的抗病毒.
  1. 首页
  3. 利用不同的距离函数来预测esmfold预测的三级结构的几何深度学习的抗病毒.

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利用不同的距离函数来预测ESMFold预测的三级结构的几何深度学习的抗病毒.

Greneter Cordoves-Delgado1, César R García-Jacas2,3, Yovani Marrero-Ponce4,5

  • 1Centro de Nanociencias y Nanotecnología, Universidad Nacional Autónoma de Mexico, Km. 107 Carretera Tijuana-Ensenada, Ensenada 22860, Baja California, Mexico.

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在PubMed 上查看摘要

概括
此摘要是机器生成的。

在结构图表中探索超出欧几里德距离的替代距离函数显著改善了抗病毒的预测. 这增强了用于药物发现的机器学习模型.

关键词:
欧洲经济机制-2 (ESM-2) 是一个.在ESMFold.在QSAR中使用QSAR.抗病毒是一种抗病毒.距离函数 距离函数进化规模建模 进化规模建模几何深度学习的几何深度学习图表深度学习深度学习

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科学领域:

  • 计算化学是一种计算化学.
  • 生物信息学是一种生物信息学.
  • 药物发现 药物发现

背景情况:

  • 机器学习加速了基于的药物发现.
  • 图形学习框架使用结构图形.
  • 目前的方法依赖于欧几里德距离值,没有强有力的证据.

研究的目的:

  • 调查结构图表生成的各种距离函数.
  • 使用这些图表来训练深度图形学习模型进行抗病毒预测.

主要方法:

  • 使用各种距离函数分析氨基酸的接近程度.
  • 根据不同的距离指标和随机图进行比较.
  • 训练和评估具有最佳图形表示的深度图形学习模型.

主要成果:

  • 不同的距离函数创建不同的图形编码不同的化学空间.
  • 这些替代图表提高了预测模型的区分能力.
  • 与最先进的模型进行了性能比较.

结论:

  • 欧几里德距离对于全面的结构图表表示是不够的.
  • 采用多种距离函数可以产生优异的图形结构.
  • 优化图形表示导致增强的抗病毒预测模型.